
Kronecker CP Decomposition with Fast Multiplication for Compressing RNNs
Recurrent neural networks (RNNs) are powerful in the tasks oriented to s...
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Hybrid Tensor Decomposition in Neural Network Compression
Deep neural networks (DNNs) have enabled impressive breakthroughs in var...
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Braininspired globallocal hybrid learning towards humanlike intelligence
The combination of neuroscienceoriented and computerscienceoriented a...
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Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differences
Neuromorphic data, recording frameless spike events, have attracted cons...
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Exploring Adversarial Attack in Spiking Neural Networks with SpikeCompatible Gradient
Recently, backpropagation through time inspired learning algorithms are ...
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A Comprehensive and Modularized Statistical Framework for Gradient Norm Equality in Deep Neural Networks
In recent years, plenty of metrics have been proposed to identify networ...
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Transfer Learning in General Lensless Imaging through Scattering Media
Recently deep neural networks (DNNs) have been successfully introduced t...
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Bridging adversarial samples and adversarial networks
Generative adversarial networks have achieved remarkable performance on ...
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Lossless Compression for 3DCNNs Based on Tensor Train Decomposition
Three dimensional convolutional neural networks (3DCNNs) have been appli...
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Comprehensive SNN Compression Using ADMM Optimization and Activity Regularization
Spiking neural network is an important family of models to emulate the b...
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Training HighPerformance and LargeScale Deep Neural Networks with Full 8bit Integers
Deep neural network (DNN) quantization converting floatingpoint (FP) da...
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A Hybrid Learning Rule for Efficient and Rapid Inference with Spiking Neural Networks
The emerging neuromorphic computing (NC) architectures have shown compel...
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A Tandem Learning Rule for Efficient and Rapid Inference on Deep Spiking Neural Networks
Emerging neuromorphic computing (NC) architectures have shown compelling...
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Deep Spiking Neural Network with Spike Count based Learning Rule
Deep spiking neural networks (SNNs) support asynchronous eventdriven co...
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Batch Normalization Sampling
Deep Neural Networks (DNNs) thrive in recent years in which Batch Normal...
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Dynamic Sparse Graph for Efficient Deep Learning
We propose to execute deep neural networks (DNNs) with dynamic and spars...
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Direct Training for Spiking Neural Networks: Faster, Larger, Better
Spiking neural networks (SNNs) are gaining more attention as a promising...
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Crossbaraware neural network pruning
Crossbar architecture based devices have been widely adopted in neural n...
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L1Norm Batch Normalization for Efficient Training of Deep Neural Networks
Batch Normalization (BN) has been proven to be quite effective at accele...
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Training and Inference with Integers in Deep Neural Networks
Researches on deep neural networks with discrete parameters and their de...
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Superresolution of spatiotemporal eventbased image
Superresolution (SR) is a useful technology to generate a highresoluti...
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SpatioTemporal Backpropagation for Training Highperformance Spiking Neural Networks
Compared with artificial neural networks (ANNs), spiking neural networks...
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Gated XNOR Networks: Deep Neural Networks with Ternary Weights and Activations under a Unified Discretization Framework
There is a pressing need to build an architecture that could subsume the...
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Realtime Tracking Based on Neuromrophic Vision
Realtime tracking is an important problem in computer vision in which m...
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Guoqi Li
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